def test_set_binary_interaction():
"""Regression test for adding binary interaction parameter with customNonbondedForce"""
# Load in cgmodel
cgmodel = pickle.load(open(f"{data_path}/stored_cgmodel.pkl", "rb"))
binary_interaction_parameters = {"bb_sc_binary_interaction": 0.0}
cgmodel_new = CGModel(
particle_type_list=cgmodel.particle_type_list,
bond_lengths=cgmodel.bond_lengths,
bond_force_constants=cgmodel.bond_force_constants,
bond_angle_force_constants=cgmodel.bond_angle_force_constants,
torsion_force_constants=cgmodel.torsion_force_constants,
equil_bond_angles=cgmodel.equil_bond_angles,
torsion_periodicities=cgmodel.torsion_periodicities,
binary_interaction_parameters=binary_interaction_parameters,
include_nonbonded_forces=cgmodel.include_nonbonded_forces,
include_bond_forces=cgmodel.include_bond_forces,
include_bond_angle_forces=cgmodel.include_bond_angle_forces,
include_torsion_forces=cgmodel.include_torsion_forces,
constrain_bonds=cgmodel.constrain_bonds,
sequence=cgmodel.sequence,
positions=cgmodel.positions,
monomer_types=cgmodel.monomer_types,
)
native_structure_file = f"{structures_path}/medoid_0.dcd"
native_traj = md.load(native_structure_file,
top=md.Topology.from_openmm(cgmodel.topology))
positions = native_traj.xyz[0] * unit.nanometer
# Minimize energy of native structure
positions, PE_start, PE_end, simulation = minimize_structure(
cgmodel_new,
positions,
output_file=f"{structures_path}/medoid_min.dcd",
)
# These should be equal to ~3 decimal places (1 Joule/mol)
PE_start_kappa_off = -47.523193359375
PE_end_kappa_off = -73.21410369873047
PE_start_kappa_on = PE_start.value_in_unit(unit.kilojoule_per_mole)
PE_end_kappa_on = PE_end.value_in_unit(unit.kilojoule_per_mole)
assert_almost_equal(PE_start_kappa_on, PE_start_kappa_off, decimal=3)
assert_almost_equal(PE_end_kappa_on, PE_end_kappa_off, decimal=3)
def get_torsion_matrix(file_list, cgmodel, frame_start, frame_stride,
frame_end, backbone_torsion_type):
"""Internal function for reading trajectory files and computing torsions"""
# Load files as {replica number: replica trajectory}
rep_traj = {}
for i in range(len(file_list)):
if file_list[0][-3:] == 'dcd':
rep_traj[i] = md.load(file_list[i],
top=md.Topology.from_openmm(
cgmodel.topology))
else:
rep_traj[i] = md.load(file_list[i])
# Combine all trajectories, selecting specified frames
if frame_end == -1:
frame_end = rep_traj[0].n_frames
if frame_start == -1:
frame_start == frame_end
traj_all = rep_traj[0][frame_start:frame_end:frame_stride]
for i in range(len(file_list) - 1):
traj_all = traj_all.join(
rep_traj[i + 1][frame_start:frame_end:frame_stride])
# Get torsion list:
torsion_list = CGModel.get_torsion_list(cgmodel)
# Assign torsion types:
torsion_types, torsion_array, torsion_sub_arrays, n_i, i_torsion_type, torsion_dict, inv_torsion_dict = \
assign_torsion_types(cgmodel, torsion_list)
# Compute specified torsion angles over all frames:
for i in range(i_torsion_type):
if inv_torsion_dict[str(i + 1)] == backbone_torsion_type:
# Compute all torsion values in trajectory
# This returns an [nframes x n_torsions] array
torsion_val_array = md.compute_dihedrals(
traj_all, torsion_sub_arrays[str(i + 1)])
# Convert to degrees:
torsion_val_array = (180 / np.pi) * torsion_val_array
return torsion_val_array, traj_all
def assign_bond_types(cgmodel, bond_list):
"""Internal function for assigning bond types"""
bond_types = []
bond_array = np.zeros((len(bond_list), 2))
# Relevant bond types are added to a dictionary as they are discovered
bond_dict = {}
# Create an inverse dictionary for getting bond string name from integer type
inv_bond_dict = {}
# Counter for number of bond types found:
i_bond_type = 0
# Assign bond types:
for i in range(len(bond_list)):
bond_array[i, 0] = bond_list[i][0]
bond_array[i, 1] = bond_list[i][1]
particle_types = [
CGModel.get_particle_type_name(cgmodel, bond_list[i][0]),
CGModel.get_particle_type_name(cgmodel, bond_list[i][1])
]
string_name = ""
reverse_string_name = ""
for particle in particle_types:
string_name += f"{particle}_"
string_name = string_name[:-1]
for particle in reversed(particle_types):
reverse_string_name += f"{particle}_"
reverse_string_name = reverse_string_name[:-1]
if (string_name in bond_dict.keys()) == False:
# New bond type found, add to bond dictionary
i_bond_type += 1
bond_dict[string_name] = i_bond_type
bond_dict[reverse_string_name] = i_bond_type
# For inverse dict we will use only the forward name based on first encounter
inv_bond_dict[str(i_bond_type)] = string_name
# print(f"adding new bond type {i_bond_type}: {string_name} to dictionary")
# print(f"adding reverse version {i_bond_type}: {reverse_string_name} to dictionary")
bond_types.append(bond_dict[string_name])
# Sort bonds by type into separate sub arrays for mdtraj compute_distances
bond_sub_arrays = {}
for i in range(i_bond_type):
bond_sub_arrays[str(i + 1)] = np.zeros((bond_types.count(i + 1), 2))
# Counter vector for all bond types
n_i = np.zeros((i_bond_type, 1), dtype=int)
for i in range(len(bond_list)):
bond_sub_arrays[str(bond_types[i])][n_i[bond_types[i] -
1], :] = bond_array[i, :]
n_i[bond_types[i] - 1] += 1
return bond_types, bond_array, bond_sub_arrays, n_i, i_bond_type, bond_dict, inv_bond_dict
"bb_bb_bb_sc_torsion_periodicity": 1,
}
# Get initial positions from local file
positions = PDBFile("24mer_1b1s_initial_structure.pdb").getPositions()
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
equil_torsion_angles=equil_torsion_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
sequence=sequence,
positions=positions,
monomer_types=[A],
)
# store the cg model so that we can do various analyses.
cgmodel.export("stored_cgmodel.pkl")
if not os.path.exists(output_data) or overwrite_files == True:
run_replica_exchange(
cgmodel.topology,
def signac_run_replica_exchange(job):
# Run replica exchange simulation for current job parameters
# Job settings
output_directory = os.path.join(job.workspace(), "output")
if not os.path.exists(output_directory):
os.mkdir(output_directory)
overwrite_files = True # overwrite files.
# Replica exchange simulation settings
total_simulation_time = 0.05 * unit.nanosecond
simulation_time_step = 10.0 * unit.femtosecond
total_steps = int(np.floor(total_simulation_time / simulation_time_step))
output_data = os.path.join(output_directory, "output.nc")
number_replicas = 36
min_temp = 100.0 * unit.kelvin
max_temp = 500.0 * unit.kelvin
temperature_list = get_temperature_list(min_temp, max_temp,
number_replicas)
exchange_frequency = 100 # Number of steps between exchange attempts
collision_frequency = 5 / unit.picosecond
include_bond_forces = True
include_bond_angle_forces = True
include_nonbonded_forces = True
include_torsion_forces = True
constrain_bonds = False
mass = 100.0 * unit.amu
# mass and charge are defaults.
bb = {
"particle_type_name": "bb",
"sigma": job.sp.sigma_bb * unit.nanometer,
"epsilon": job.sp.epsilon_bb * unit.kilojoules_per_mole,
"mass": mass
}
sc = {
"particle_type_name": "sc",
"sigma": job.sp.sigma_sc * unit.nanometer,
"epsilon": job.sp.epsilon_sc * unit.kilojoules_per_mole,
"mass": mass
}
# Monomer definition
A = {
"monomer_name": "A",
"particle_sequence": [bb, sc],
"bond_list": [[0, 1]],
"start": 0,
"end": 0,
}
sequence = 24 * [A]
# Bond definitions
bond_lengths = {
"default_bond_length": job.sp.equil_bond_length * unit.nanometer
}
bond_force_constants = {
"default_bond_force_constant":
job.sp.k_bond * unit.kilojoule_per_mole / unit.nanometer /
unit.nanometer
}
# Bond angle definitions
bond_angle_force_constants = {
"default_bond_angle_force_constant":
job.sp.k_angle * unit.kilojoule_per_mole / unit.radian / unit.radian
}
equil_bond_angles = {
"default_equil_bond_angle": job.sp.equil_bond_angle * unit.degrees
}
# torsion angle definitions
torsion_force_constants = {
"default_torsion_force_constant":
0.0 * unit.kilojoule_per_mole,
"bb_bb_bb_bb_torsion_force_constant":
job.sp.k_torsion * unit.kilojoule_per_mole
}
torsion_phase_angles = {
"sc_bb_bb_sc_torsion_phase_angle": 0 * unit.degrees,
"bb_bb_bb_bb_torsion_phase_angle":
job.sp.torsion_phase_angle * unit.degrees,
"bb_bb_bb_sc_torsion_phase_angle": 0 * unit.degrees,
}
torsion_periodicities = {
"sc_bb_bb_sc_torsion_periodicity": job.sp.torsion_periodicity,
"bb_bb_bb_bb_torsion_periodicity": job.sp.torsion_periodicity,
"bb_bb_bb_sc_torsion_periodicity": job.sp.torsion_periodicity,
}
# Get initial positions from local file
pdb_path = os.path.join(proj_directory, "24mer_1b1s_initial_structure.pdb")
positions = PDBFile(pdb_path).getPositions()
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
sequence=sequence,
positions=positions,
monomer_types=[A],
)
# store the cg model so that we can do various analyses.
cgmodel.export(job.fn("stored_cgmodel.pkl"))
if not os.path.exists(output_data) or overwrite_files == True:
run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
friction=collision_frequency,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
total_simulation_time=total_simulation_time,
exchange_frequency=exchange_frequency,
output_data=output_data,
)
else:
print("Replica output files exist")
def calc_torsion_distribution(
cgmodel, file_list, nbins=180, frame_start=0, frame_stride=1, frame_end=-1,
plot_per_page=2, temperature_list=None, plotfile="torsion_hist.pdf"
):
"""
Calculate and plot all torsion distributions from a CGModel object and pdb or dcd trajectory
:param cgmodel: CGModel() object
:type cgmodel: class
:param file_list: path to pdb or dcd trajectory file(s)
:type file_list: str or list(str)
:param nbins: number of bins spanning the range of -180 to 180 degrees, default = 180
:type nbins: int
:param frame_start: First frame in trajectory file to use for analysis.
:type frame_start: int
:param frame_stride: Advance by this many frames when reading trajectories.
:type frame_stride: int
:param frame_end: Last frame in trajectory file to use for analysis.
:type frame_end: int
:param plot_per_page: number of subplots to display on each page (default=2)
:type plot_per_page: int
:param temperature_list: list of temperatures corresponding to file_list. If None, file names will be the plot labels.
:type temperature_list: list(Quantity())
:param plotfile: Base filename for saving torsion distribution pdf plots
:type plotfile: str
:returns:
- torsion_hist_data ( dict )
"""
# Convert file_list to list if a single string:
if type(file_list) == str:
# Single file
file_list = file_list.split()
# Get torsion list
torsion_list = CGModel.get_torsion_list(cgmodel)
# Assign torsion types
torsion_types, torsion_array, torsion_sub_arrays, n_i, i_torsion_type, torsion_dict, inv_torsion_dict = \
assign_torsion_types(cgmodel, torsion_list)
# Create dictionary for saving torsion histogram data:
torsion_hist_data = {}
# Set bin edges:
torsion_bin_edges = np.linspace(-180,180,nbins+1)
torsion_bin_centers = np.zeros((len(torsion_bin_edges)-1,1))
for i in range(len(torsion_bin_edges)-1):
torsion_bin_centers[i] = (torsion_bin_edges[i]+torsion_bin_edges[i+1])/2
file_index = 0
for file in file_list:
# Load in a trajectory file:
if file[-3:] == 'dcd':
traj = md.load(file,top=md.Topology.from_openmm(cgmodel.topology))
else:
traj = md.load(file)
# Select frames for analysis:
if frame_end == -1:
frame_end = traj.n_frames
traj = traj[frame_start:frame_end:frame_stride]
nframes = traj.n_frames
# Create inner dictionary for current file:
if temperature_list is not None:
file_key = f"{temperature_list[file_index].value_in_unit(unit.kelvin):.2f}"
else:
file_key = file[:-4]
torsion_hist_data[file_key] = {}
for i in range(i_torsion_type):
# Compute all torsion values in trajectory
# This returns an [nframes x n_torsions] array
torsion_val_array = md.compute_dihedrals(
traj,torsion_sub_arrays[str(i+1)])
# Reshape arrays and convert to degrees:
torsion_val_array = (180/np.pi)*np.reshape(torsion_val_array, (nframes*n_i[i][0],1))
# Histogram and plot results:
n_out, bin_edges_out = np.histogram(
torsion_val_array, bins=torsion_bin_edges,density=True)
torsion_hist_data[file_key][f"{inv_torsion_dict[str(i+1)]}_density"]=n_out
torsion_hist_data[file_key][f"{inv_torsion_dict[str(i+1)]}_bin_centers"]=torsion_bin_centers
file_index += 1
plot_distribution(
inv_torsion_dict,
torsion_hist_data,
xlabel="Torsion angle (degrees)",
ylabel="Probability density",
xlim=[-180,180],
figure_title="Torsion_distributions",
file_name=f"{plotfile}",
plot_per_page=plot_per_page,
)
return torsion_hist_data
def calc_ramachandran(
cgmodel,
file_list,
nbin_theta=180,
nbin_alpha=180,
frame_start=0,
frame_stride=1,
frame_end=-1,
plotfile="ramachandran.pdf",
backbone_angle_type = "bb_bb_bb",
backbone_torsion_type = "bb_bb_bb_bb",
colormap="nipy_spectral",
temperature_list=None,
):
"""
Calculate and plot ramachandran plot for backbone bond bending-angle and torsion
angle, given a CGModel object and pdb or dcd trajectory.
:param cgmodel: CGModel() object
:type cgmodel: class
:param file_list: path to pdb or dcd trajectory file(s) - can be a list or single string
:type file_list: str or list(str)
:param nbin_theta: number of bins for bond-bending angle (spanning from 0 to 180 degrees)
:type nbin_theta: int
:param nbin_alpha: number of bins for torsion angle (spanning from -180 to +180 degrees)
:type nbin_alpha:
:param frame_start: First frame in trajectory file to use for analysis.
:type frame_start: int
:param frame_stride: Advance by this many frames when reading trajectories.
:type frame_stride: int
:param frame_end: Last frame in trajectory file to use for analysis.
:type frame_end: int
:param plotfile: Filename for saving torsion distribution pdf plots
:type plotfile: str
:param backbone_angle_type: particle sequence of the backbone angles (default="bb_bb_bb") - for now only single sequence permitted
:type backbone_angle_type: str
:param backbone_torsion_type: particle sequence of the backbone angles (default="bb_bb_bb_bb") - for now only single sequence permitted
:type backbone_torsion_type: str
:param colormap: matplotlib pyplot colormap to use (default='nipy_spectral')
:type colormap: str (case sensitive)
:param temperature_list: list of temperatures corresponding to file_list. If None, no subplot labels will be used.
:type temperature_list: list(Quantity())
:returns:
- hist_data ( dict )
- xedges ( dict )
- yedges ( dict )
"""
# Convert file_list to list if a single string:
if type(file_list) == str:
# Single file
file_list = file_list.split()
# Store angle, torsion values by filename for computing global colormap
ang_val_array = {}
torsion_val_array = {}
for file in file_list:
# Load in a trajectory file:
if file[-3:] == 'dcd':
traj = md.load(file,top=md.Topology.from_openmm(cgmodel.topology))
else:
traj = md.load(file)
# Select frames for analysis:
if frame_end == -1:
frame_end = traj.n_frames
traj = traj[frame_start:frame_end:frame_stride]
nframes = traj.n_frames
# Get angle list
angle_list = CGModel.get_bond_angle_list(cgmodel)
# Assign angle types:
ang_types, ang_array, ang_sub_arrays, n_i, i_angle_type, ang_dict, inv_ang_dict = \
assign_angle_types(cgmodel, angle_list)
# Set bin edges:
angle_bin_edges = np.linspace(0,180,nbin_theta+1)
angle_bin_centers = np.zeros((len(angle_bin_edges)-1,1))
for i in range(len(angle_bin_edges)-1):
angle_bin_centers[i] = (angle_bin_edges[i]+angle_bin_edges[i+1])/2
for i in range(i_angle_type):
if inv_ang_dict[str(i+1)] == backbone_angle_type:
# Compute all angle values in trajectory
# This returns an [nframes x n_angles] array
ang_val_array[file] = md.compute_angles(traj,ang_sub_arrays[str(i+1)])
# We will have different numbers of bond-bending angle and torsion angle.
# We will set a convention of omitting the last angle value.
# Convert to degrees and exclude last angle:
ang_val_array[file] = (180/np.pi)*ang_val_array[file][:,:-1]
# Reshape array:
ang_val_array[file] = np.reshape(ang_val_array[file], (nframes*(n_i[i]-1)[0],1))
# Get torsion list
torsion_list = CGModel.get_torsion_list(cgmodel)
# Assign torsion types
torsion_types, torsion_array, torsion_sub_arrays, n_j, i_torsion_type, torsion_dict, inv_torsion_dict = \
assign_torsion_types(cgmodel, torsion_list)
# Set bin edges:
torsion_bin_edges = np.linspace(-180,180,nbin_alpha+1)
torsion_bin_centers = np.zeros((len(torsion_bin_edges)-1,1))
for i in range(len(torsion_bin_edges)-1):
torsion_bin_centers[i] = (torsion_bin_edges[i]+torsion_bin_edges[i+1])/2
for i in range(i_torsion_type):
if inv_torsion_dict[str(i+1)] == backbone_torsion_type:
# Compute all torsion values in trajectory
# This returns an [nframes x n_torsions] array
torsion_val_array[file] = md.compute_dihedrals(
traj,torsion_sub_arrays[str(i+1)])
# Convert to degrees:
torsion_val_array[file] *= (180/np.pi)
# Reshape array
torsion_val_array[file] = np.reshape(torsion_val_array[file], (nframes*n_j[i][0],1))
# 2d histogram the data and plot:
hist_data, xedges, yedges = plot_2d_distribution(
file_list, torsion_val_array, ang_val_array, torsion_bin_edges, angle_bin_edges,
plotfile, colormap, xlabel='Alpha (degrees)', ylabel='Theta (degrees)', temperature_list=temperature_list)
return hist_data, xedges, yedges
torsion_periodicities = {"bb_bb_bb_bb_period": 1} # ,'sc_bb_bb_sc_period': 2}
# Initiate cgmodel using positions from local file
positions = PDBFile("init.pdb").getPositions()
# Build a coarse grained model using the positions for the initial structure
cgmodel = CGModel(
polymer_length=polymer_length,
backbone_lengths=backbone_lengths,
sidechain_lengths=sidechain_lengths,
sidechain_positions=sidechain_positions,
masses=masses,
sigmas=sigmas,
epsilons=epsilons,
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
torsion_force_constants=torsion_force_constants,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
positions=positions,
)
if os.path.exists(output_data):
# Search for existing data, and read it if possible
replica_energies, replica_positions, replica_states = read_replica_exchange_data(
system=cgmodel.system,
topology=cgmodel.topology,
"sigmas": sigmas,
}
monomer_types = [A, B]
sequence = [A, A, A, B, A, A, A, B, A, A, A, B]
polymer_length = len(sequence)
cgmodel = CGModel(
polymer_length=polymer_length,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
equil_torsion_angles=equil_torsion_angles,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
heteropolymer=True,
monomer_types=monomer_types,
sequence=sequence,
)
if not os.path.exists(output_data):
replica_energies, replica_positions, replica_states = run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
wall_clock_time_list = []
for polymer_length in [8, 10, 20, 30]:
print("Running simulations with 'polymer_length' =" + str(polymer_length))
cg_model = CGModel(
polymer_length=polymer_length,
backbone_lengths=backbone_lengths,
sidechain_lengths=sidechain_lengths,
sidechain_positions=sidechain_positions,
masses=masses,
sigmas=sigmas,
epsilons=epsilons,
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
random_positions=True,
)
output_data = str(str(output_directory) + "/" + str(polymer_length))
if not os.path.exists(output_data):
os.mkdir(output_data)
for i in range(sigma_increments)
]
df_ij_list = []
ddf_ij_list = []
Delta_u_list = []
dDelta_u_list = []
Delta_s_list = []
dDelta_s_list = []
C_v_list = []
dC_v_list = []
for sigma in sigma_list:
output_data = str(str(top_directory) + "/rep_ex_" + str(sigma) + ".nc")
sigmas = {"bb_bb_sigma": sigma, "bb_sc_sigma": sigma, "sc_sc_sigma": sigma}
cgmodel = CGModel(sigmas=sigmas)
if not os.path.exists(output_data):
print(
"Performing simulations and free energy analysis for a coarse grained model"
)
print("with sigma values of " + str(sigma))
replica_energies, replica_positions, replica_states = run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
total_simulation_time=total_simulation_time,
print_frequency=print_frequency,
output_data=output_data,
)
simulation_time_step = 5.0 * unit.femtosecond
total_steps = round(total_simulation_time.__div__(simulation_time_step))
# Yank (replica exchange) simulation settings
output_data = str(str(top_directory) + "/output.nc")
number_replicas = 20
min_temp = 50.0 * unit.kelvin
max_temp = 300.0 * unit.kelvin
temperature_list = get_temperature_list(min_temp, max_temp, number_replicas)
print("Using " + str(len(temperature_list)) + " replicas.")
if total_steps > 10000:
exchange_attempts = round(total_steps / 1000)
else:
exchange_attempts = 10
cgmodel = CGModel()
if not os.path.exists(output_data):
replica_energies, replica_positions, replica_states = run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
total_simulation_time=total_simulation_time,
print_frequency=print_frequency,
output_data=output_data,
)
make_replica_pdb_files(cgmodel.topology, replica_positions)
else:
replica_energies, replica_positions, replica_states = read_replica_exchange_data(
def test_sums_periodic_torsions_5():
# Test cg_model with sums of periodic torsions - test 5
# Two periodic torsion terms, parameters input as quantities with list values
# Parameters are applied to all torsion types using the default input method
# Coarse grained model settings
include_bond_forces = True
include_bond_angle_forces = True
include_nonbonded_forces = True
include_torsion_forces = True
constrain_bonds = False
# Bond definitions
bond_length = 1.5 * unit.angstrom
bond_lengths = {
"bb_bb_bond_length": bond_length,
"bb_sc_bond_length": bond_length,
"sc_sc_bond_length": bond_length,
}
bond_force_constant = 1000 * unit.kilojoule_per_mole / unit.nanometer / unit.nanometer
bond_force_constants = {
"bb_bb_bond_force_constant": bond_force_constant,
"bb_sc_bond_force_constant": bond_force_constant,
"sc_sc_bond_force_constant": bond_force_constant,
}
# Particle definitions
mass = 100.0 * unit.amu
r_min = 1.5 * bond_length # Lennard-Jones potential r_min
# Factor of /(2.0**(1/6)) is applied to convert r_min to sigma
sigma = r_min / (2.0**(1.0 / 6.0))
epsilon = 0.5 * unit.kilojoule_per_mole
bb = {
"particle_type_name": "bb",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
sc = {
"particle_type_name": "sc",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
# Bond angle definitions
bond_angle_force_constant = 100 * unit.kilojoule_per_mole / unit.radian / unit.radian
bond_angle_force_constants = {
"bb_bb_bb_bond_angle_force_constant": bond_angle_force_constant,
"bb_bb_sc_bond_angle_force_constant": bond_angle_force_constant,
}
# OpenMM requires angle definitions in units of radians
bb_bb_bb_equil_bond_angle = 120.0 * unit.degrees
bb_bb_sc_equil_bond_angle = 120.0 * unit.degrees
equil_bond_angles = {
"bb_bb_bb_equil_bond_angle": bb_bb_bb_equil_bond_angle,
"bb_bb_sc_equil_bond_angle": bb_bb_sc_equil_bond_angle,
}
# Torsion angle definitions
torsion_force_constants = {
"default_torsion_force_constant": [5, 10] * unit.kilojoule_per_mole,
}
torsion_phase_angles = {
"default_torsion_phase_angle": [0, 180] * unit.degrees,
}
torsion_periodicities = {
"default_torsion_periodicity": [1, 3],
}
# Monomer definitions
A = {
"monomer_name": "A",
"particle_sequence": [bb, sc],
"bond_list": [[0, 1]],
"start": 0,
"end": 0,
}
sequence = 24 * [A]
pdb_path = os.path.join(data_path, "24mer_1b1s_initial_structure.pdb")
positions = PDBFile(pdb_path).getPositions()
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
positions=positions,
sequence=sequence,
monomer_types=[A],
)
# Check the number of periodic torsions terms:
n_torsion_forces = cgmodel.system.getForces()[3].getNumTorsions()
assert n_torsion_forces == 176
def create_cgmodel():
# Coarse grained model settings
include_bond_forces = True
include_bond_angle_forces = True
include_nonbonded_forces = True
include_torsion_forces = True
constrain_bonds = False
# Bond definitions
bond_length = 1.5 * unit.angstrom
bond_lengths = {
"bb_bb_bond_length": bond_length,
"bb_sc_bond_length": bond_length,
"sc_sc_bond_length": bond_length,
}
bond_force_constant = 1000 * unit.kilojoule_per_mole / unit.nanometer / unit.nanometer
bond_force_constants = {
"bb_bb_bond_force_constant": bond_force_constant,
"bb_sc_bond_force_constant": bond_force_constant,
"sc_sc_bond_force_constant": bond_force_constant,
}
# Particle definitions
mass = 100.0 * unit.amu
r_min = 1.5 * bond_length # Lennard-Jones potential r_min
# Factor of /(2.0**(1/6)) is applied to convert r_min to sigma
sigma = r_min / (2.0**(1.0 / 6.0))
epsilon = 0.5 * unit.kilojoule_per_mole
bb = {
"particle_type_name": "bb",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
sc = {
"particle_type_name": "sc",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
# Bond angle definitions
bond_angle_force_constant = 100 * unit.kilojoule_per_mole / unit.radian / unit.radian
bond_angle_force_constants = {
"bb_bb_bb_bond_angle_force_constant": bond_angle_force_constant,
"bb_bb_sc_bond_angle_force_constant": bond_angle_force_constant,
}
# OpenMM requires angle definitions in units of radians
bb_bb_bb_equil_bond_angle = 120.0 * unit.degrees
bb_bb_sc_equil_bond_angle = 120.0 * unit.degrees
equil_bond_angles = {
"bb_bb_bb_equil_bond_angle": bb_bb_bb_equil_bond_angle,
"bb_bb_sc_equil_bond_angle": bb_bb_sc_equil_bond_angle,
}
# Torsion angle definitions
torsion_force_constant = 20.0 * unit.kilojoule_per_mole
torsion_force_constants = {
"bb_bb_bb_bb_torsion_force_constant": torsion_force_constant,
"bb_bb_bb_sc_torsion_force_constant": torsion_force_constant
}
bb_bb_bb_bb_torsion_phase_angle = 75.0 * unit.degrees
bb_bb_bb_sc_torsion_phase_angle = 75.0 * unit.degrees
torsion_phase_angles = {
"bb_bb_bb_bb_torsion_phase_angle": bb_bb_bb_bb_torsion_phase_angle,
"bb_bb_bb_sc_torsion_phase_angle": bb_bb_bb_sc_torsion_phase_angle
}
torsion_periodicities = {
"bb_bb_bb_bb_torsion_periodicity": 3,
"bb_bb_bb_sc_torsion_periodicity": 3
}
# Monomer definitions
A = {
"monomer_name": "A",
"particle_sequence": [bb, sc],
"bond_list": [[0, 1]],
"start": 0,
"end": 0,
}
sequence = 24 * [A]
pdb_path = os.path.join(data_path, "24mer_1b1s_initial_structure.pdb")
positions = PDBFile(pdb_path).getPositions()
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
positions=positions,
sequence=sequence,
monomer_types=[A],
)
return cgmodel
def calc_bond_length_distribution(cgmodel,
file_list,
nbins=90,
frame_start=0,
frame_stride=1,
frame_end=-1,
plot_per_page=2,
temperature_list=None,
plotfile="bond_hist.pdf"):
"""
Calculate and plot all bond length distributions from a CGModel object and trajectory
:param cgmodel: CGModel() object
:type cgmodel: class
:param file_list: path to pdb or dcd trajectory file(s)
:type file_list: str or list(str)
:param nbins: number of histogram bins
:type nbins: int
:param frame_start: First frame in trajectory file to use for analysis.
:type frame_start: int
:param frame_stride: Advance by this many frames when reading trajectories.
:type frame_stride: int
:param frame_end: Last frame in trajectory file to use for analysis.
:type frame_end: int
:param plot_per_page: number of subplots to display on each page (default=2)
:type plot_per_page: int
:param temperature_list: list of temperatures corresponding to file_list. If None, file names will be the plot labels.
:type temperature_list: list(Quantity())
:param plotfile: filename for saving bond length distribution pdf plots
:type plotfile: str
:returns:
- bond_hist_data ( dict )
"""
# Convert file_list to list if a single string:
if type(file_list) == str:
# Single file
file_list = file_list.split()
# Create dictionary for saving bond histogram data:
bond_hist_data = {}
# Get bond list
bond_list = CGModel.get_bond_list(cgmodel)
# Assign bond types:
bond_types, bond_array, bond_sub_arrays, n_i, i_bond_type, bond_dict, inv_bond_dict = \
assign_bond_types(cgmodel, bond_list)
file_index = 0
for file in file_list:
# Load in a trajectory file:
if file[-3:] == 'dcd':
traj = md.load(file, top=md.Topology.from_openmm(cgmodel.topology))
else:
traj = md.load(file)
# Select frames for analysis:
if frame_end == -1:
frame_end = traj.n_frames
traj = traj[frame_start:frame_end:frame_stride]
nframes = traj.n_frames
# Create inner dictionary for current file:
if temperature_list is not None:
file_key = f"{temperature_list[file_index].value_in_unit(unit.kelvin):.2f}"
else:
file_key = file[:-4]
bond_hist_data[file_key] = {}
for i in range(i_bond_type):
# Compute all bond distances in trajectory
# This returns an [nframes x n_bonds] array
bond_val_array = md.compute_distances(traj,
bond_sub_arrays[str(i + 1)])
# Reshape arrays:
bond_val_array = np.reshape(bond_val_array,
(nframes * n_i[i][0], 1))
# Histogram and plot results:
n_out, bin_edges_out = np.histogram(bond_val_array,
bins=nbins,
density=True)
bond_bin_centers = np.zeros((len(bin_edges_out) - 1, 1))
for j in range(len(bin_edges_out) - 1):
bond_bin_centers[j] = (bin_edges_out[j] +
bin_edges_out[j + 1]) / 2
bond_hist_data[file_key][
f"{inv_bond_dict[str(i+1)]}_density"] = n_out
bond_hist_data[file_key][
f"{inv_bond_dict[str(i+1)]}_bin_centers"] = bond_bin_centers
file_index += 1
plot_distribution(
inv_bond_dict,
bond_hist_data,
xlabel="Bond length (nm)",
ylabel="Probability density",
figure_title="Bond distributions",
file_name=f"{plotfile}",
plot_per_page=plot_per_page,
)
return bond_hist_data
"start": 0,
"end": 1
}
sequence = 12 * [A]
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
sequence=sequence,
constrain_bonds=constrain_bonds,
random_positions=random_positions,
monomer_types=[A],
)
file_name = "12mer_2b1s_initial_structure.pdb"
# file_name = "12mer_1b1s_initial_structure.pdb"
# file_name = "24mer_1b1s_initial_structure.pdb"
# file_name = "12mer_1b2s_initial_structure.pdb" # this one looks a bit off, and takes a long time
# file_name = "12mer_2b1s_initial_structure.pdb"
# file_name = "12mer_2b2s_initial_structure.pdb"
def test_random_builder(tmpdir):
"""See if the random builder can build a simple 1b1s model"""
# Coarse grained model settings
include_bond_forces = True
include_bond_angle_forces = True
include_nonbonded_forces = True
include_torsion_forces = False
constrain_bonds = False
random_positions = True
# Bond definitions
bond_length = 1.5 * unit.angstrom
bond_lengths = {
"bb_bb_bond_length": bond_length,
"bb_sc_bond_length": bond_length,
"sc_sc_bond_length": bond_length,
}
bond_force_constant = 1000 * unit.kilojoule_per_mole / unit.nanometer / unit.nanometer
bond_force_constants = {
"bb_bb_bond_force_constant": bond_force_constant,
"bb_sc_bond_force_constant": bond_force_constant,
"sc_sc_bond_force_constant": bond_force_constant,
}
# Particle definitions
mass = 100.0 * unit.amu
r_min = 1.5 * bond_length # Lennard-Jones potential r_min
# Factor of /(2.0**(1/6)) is applied to convert r_min to sigma
sigma = r_min / (2.0 ** (1.0 / 6.0))
epsilon = 0.5 * unit.kilojoule_per_mole
bb = {"particle_type_name": "bb", "sigma": sigma, "epsilon": epsilon, "mass": mass}
sc = {"particle_type_name": "sc", "sigma": sigma, "epsilon": epsilon, "mass": mass}
# Bond angle definitions
bond_angle_force_constant = 100 * unit.kilojoule_per_mole / unit.radian / unit.radian
bond_angle_force_constants = {
"bb_bb_bb_bond_angle_force_constant": bond_angle_force_constant,
"bb_bb_sc_bond_angle_force_constant": bond_angle_force_constant,
}
# OpenMM requires angle definitions in units of radians
bb_bb_bb_equil_bond_angle = 120.0 * unit.degrees
bb_bb_sc_equil_bond_angle = 120.0 * unit.degrees
equil_bond_angles = {
"bb_bb_bb_equil_bond_angle": bb_bb_bb_equil_bond_angle,
"bb_bb_sc_equil_bond_angle": bb_bb_sc_equil_bond_angle,
}
# Torsion angle definitions
torsion_force_constant = 20.0 * unit.kilojoule_per_mole
torsion_force_constants = {
"bb_bb_bb_bb_torsion_force_constant": torsion_force_constant,
"bb_bb_bb_sc_torsion_force_constant": torsion_force_constant
}
bb_bb_bb_bb_torsion_phase_angle = 75.0 * unit.degrees
bb_bb_bb_sc_torsion_phase_angle = 75.0 * unit.degrees
torsion_phase_angles = {
"bb_bb_bb_bb_torsion_phase_angle": bb_bb_bb_bb_torsion_phase_angle,
"bb_bb_bb_sc_torsion_phase_angle": bb_bb_bb_sc_torsion_phase_angle
}
torsion_periodicities = {
"bb_bb_bb_bb_torsion_periodicity": 3,
"bb_bb_bb_sc_torsion_periodicity": 3}
# Monomer definitions
A = {
"monomer_name": "A",
"particle_sequence": [bb, sc],
"bond_list": [[0, 1]],
"start": 0,
"end": 0,
}
sequence = 5 * [A]
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb,sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
sequence=sequence,
constrain_bonds=constrain_bonds,
random_positions=random_positions,
monomer_types=[A],
)
output_directory = tmpdir.mkdir("output")
filename = f"{output_directory}/5mer_1b1s_builder_test.pdb"
write_pdbfile_without_topology(cgmodel, filename)
positions = PDBFile(filename).getPositions()
assert len(positions)==10
def signac_run_CEI_replica_exchange(job):
# Run replica exchange simulation for current job parameters
print(f'job_parameters:')
print(job.sp)
rep_exch_begin = time.perf_counter()
# Job settings
output_directory = os.path.join(job.workspace(),"output_CEI")
if not os.path.exists(output_directory):
os.mkdir(output_directory)
overwrite_files = True # overwrite files.
global_context_cache.platform = openmm.Platform.getPlatformByName("CUDA")
# Replica exchange simulation settings
total_simulation_time = 200.0 * unit.nanosecond
simulation_time_step = 5.0 * unit.femtosecond
total_steps = int(np.floor(total_simulation_time / simulation_time_step))
output_data = os.path.join(output_directory, "output.nc")
number_replicas = job.sp.n_replica
min_temp = 200.0 * unit.kelvin
max_temp = 600.0 * unit.kelvin
# Load in CEI temperature list:
temperature_list = pickle.load(open(job.fn("opt_T_spacing.pkl"),"rb"))
exchange_frequency = job.sp.exch_freq # Number of steps between exchange attempts
collision_frequency = job.sp.coll_freq/unit.picosecond
include_bond_forces = True
include_bond_angle_forces = True
include_nonbonded_forces = True
include_torsion_forces = True
constrain_bonds = False
mass = 100.0 * unit.amu
# mass and charge are defaults.
bb = {
"particle_type_name": "bb",
"sigma": job.sp.sigma_bb * unit.angstrom,
"epsilon": job.sp.epsilon_bb * unit.kilojoules_per_mole,
"mass": mass
}
sc = {
"particle_type_name": "sc",
"sigma": job.sp.sigma_sc * unit.angstrom,
"epsilon": job.sp.epsilon_sc * unit.kilojoules_per_mole,
"mass": mass
}
# Monomer definition
A = {
"monomer_name": "A",
"particle_sequence": [bb, sc],
"bond_list": [[0, 1]],
"start": 0,
"end": 0,
}
sequence = 24 * [A]
# Bond definitions
bond_lengths = {"default_bond_length": job.sp.equil_bond_length * unit.nanometer}
bond_force_constants = {
"default_bond_force_constant": job.sp.k_bond * unit.kilojoule_per_mole / unit.nanometer / unit.nanometer
}
# Bond angle definitions
bond_angle_force_constants = {
"default_bond_angle_force_constant": job.sp.k_angle * unit.kilojoule_per_mole / unit.radian / unit.radian
}
equil_bond_angles = {
"default_equil_bond_angle": job.sp.equil_bond_angle_bb_bb_sc * unit.degrees,
"bb_bb_bb_equil_bond_angle": job.sp.equil_bond_angle_bb_bb_bb * unit.degrees}
# torsion angle definitions
torsion_force_constants = {
"default_torsion_force_constant": 0.0 * unit.kilojoule_per_mole,
"bb_bb_bb_bb_torsion_force_constant": job.sp.k_torsion * unit.kilojoule_per_mole}
# Need to substract 180 degrees from specified torsion for mdtraj consistency
torsion_phase_angles = {
"sc_bb_bb_sc_torsion_phase_angle": 0 * unit.degrees,
"bb_bb_bb_bb_torsion_phase_angle": (job.sp.equil_torsion_angle_bb_bb_bb_bb-180) * unit.degrees,
"bb_bb_bb_sc_torsion_phase_angle": 0 * unit.degrees,
}
torsion_periodicities = {
"sc_bb_bb_sc_torsion_periodicity": job.sp.torsion_periodicity,
"bb_bb_bb_bb_torsion_periodicity": job.sp.torsion_periodicity,
"bb_bb_bb_sc_torsion_periodicity": job.sp.torsion_periodicity,
}
# Get initial positions from local file
pdb_path = os.path.join(proj_directory, f"initial_structure_trial_{job.sp.trial}.pdb")
positions = PDBFile(pdb_path).getPositions()
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
positions=positions,
sequence=sequence,
monomer_types=[A],
)
# store the cg model so that we can do various analyses.
cgmodel.export(job.fn("stored_cgmodel.pkl"))
if not os.path.exists(output_data) or overwrite_files == True:
run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
friction=collision_frequency,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
total_simulation_time=total_simulation_time,
exchange_frequency=exchange_frequency,
output_data=output_data,
)
else:
print("Replica output files exist")
rep_exch_end = time.perf_counter()
print(f'replica exchange run time: {rep_exch_end-rep_exch_begin}')
def calc_2d_distribution(
cgmodel,
file_list,
nbin_xvar=180,
nbin_yvar=180,
frame_start=0,
frame_stride=1,
frame_end=-1,
plotfile="2d_hist.pdf",
xvar_name = "bb_bb_bb",
yvar_name = "bb_bb_bb_bb",
colormap="nipy_spectral",
temperature_list=None,
):
"""
Calculate and plot 2d histogram for any 2 bonded variables,
given a CGModel object and pdb or dcd trajectory.
:param cgmodel: CGModel() object
:type cgmodel: class
:param file_list: path to pdb or dcd trajectory file(s) - can be a list or single string
:type file_list: str or list(str)
:param nbin_xvar: number of bins for x bonded variable
:type nbin_xvar: int
:param nbin_yvar: number of bins for y bonded variable
:type nbin_yvar:
:param frame_start: First frame in trajectory file to use for analysis.
:type frame_start: int
:param frame_stride: Advance by this many frames when reading trajectories.
:type frame_stride: int
:param frame_end: Last frame in trajectory file to use for analysis.
:type frame_end: int
:param plotfile: Filename for saving torsion distribution pdf plots
:type plotfile: str
:param xvar_name: particle sequence of the x bonded parameter (default="bb_bb_bb")
:type xvar_name: str
:param yvar_name: particle sequence of the y bonded parameter (default="bb_bb_bb_bb")
:type yvar_name: str
:param colormap: matplotlib pyplot colormap to use (default='nipy_spectral')
:type colormap: str (case sensitive)
:param temperature_list: list of temperatures corresponding to file_list. If None, no subplot labels will be used.
:type temperature_list: list(Quantity())
:returns:
- hist_data ( dict )
- xedges ( dict )
- yedges ( dict )
"""
# Convert file_list to list if a single string:
if type(file_list) == str:
# Single file
file_list = file_list.split()
# Store angle, torsion values by filename for computing global colormap
xvar_val_array = {}
yvar_val_array = {}
# Store the reverse name of the bonded type (need to check both)
# x variable
particle_list = []
particle = ""
for c in xvar_name:
if c == '_':
particle_list.append(particle)
particle = ""
else:
particle += c
particle_list.append(particle)
particle_list_reverse = particle_list[::-1]
xvar_name_reverse = ""
for par in particle_list_reverse:
xvar_name_reverse += par
xvar_name_reverse += "_"
xvar_name_reverse = xvar_name_reverse[:-1]
# y variable
particle_list = []
particle = ""
for c in yvar_name:
if c == '_':
particle_list.append(particle)
particle = ""
else:
particle += c
particle_list.append(particle)
particle_list_reverse = particle_list[::-1]
yvar_name_reverse = ""
for par in particle_list_reverse:
yvar_name_reverse += par
yvar_name_reverse += "_"
yvar_name_reverse = yvar_name_reverse[:-1]
for file in file_list:
# Load in a trajectory file:
if file[-3:] == 'dcd':
traj = md.load(file,top=md.Topology.from_openmm(cgmodel.topology))
else:
traj = md.load(file)
# Select frames for analysis:
if frame_end == -1:
frame_end = traj.n_frames
traj = traj[frame_start:frame_end:frame_stride]
nframes = traj.n_frames
# x variable
# Determine parameter type of xvar:
n_particle_x = xvar_name.count('_')+1
if n_particle_x == 2:
# Bond
# Get bond list
bond_list = CGModel.get_bond_list(cgmodel)
# Assign bond types:
bond_types, bond_array, bond_sub_arrays, n_i, i_bond_type, bond_dict, inv_bond_dict = \
assign_bond_types(cgmodel, bond_list)
for i in range(i_bond_type):
if inv_bond_dict[str(i+1)] == xvar_name or inv_bond_dict[str(i+1)] == xvar_name_reverse:
# Compute all bond length values in trajectory
# This returns an [nframes x n_bonds] array
xvar_val_array[file] = md.compute_distances(traj,bond_sub_arrays[str(i+1)])
# Get equilibrium value:
b_eq = cgmodel.get_bond_length(bond_sub_arrays[str(i+1)][0])
# Set bin edges:
# This should be the same across all files - use heuristic from equilibrium bond length
b_min = 0.5*b_eq.value_in_unit(unit.nanometer)
b_max = 1.5*b_eq.value_in_unit(unit.nanometer)
xvar_bin_edges = np.linspace(b_min,b_max,nbin_xvar+1)
xvar_bin_centers = np.zeros((len(xvar_bin_edges)-1,1))
for i in range(len(xvar_bin_edges)-1):
xvar_bin_centers[i] = (xvar_bin_edges[i]+xvar_bin_edges[i+1])/2
xlabel = f'{xvar_name} distance ({unit.nanometer})'
elif n_particle_x == 3:
# Angle
# Get angle list
angle_list = CGModel.get_bond_angle_list(cgmodel)
# Assign angle types:
ang_types, ang_array, ang_sub_arrays, n_i, i_angle_type, ang_dict, inv_ang_dict = \
assign_angle_types(cgmodel, angle_list)
# Set bin edges:
xvar_bin_edges = np.linspace(0,180,nbin_xvar+1)
xvar_bin_centers = np.zeros((len(xvar_bin_edges)-1,1))
for i in range(len(xvar_bin_edges)-1):
xvar_bin_centers[i] = (xvar_bin_edges[i]+xvar_bin_edges[i+1])/2
for i in range(i_angle_type):
if inv_ang_dict[str(i+1)] == xvar_name or inv_ang_dict[str(i+1)] == xvar_name_reverse:
# Compute all angle values in trajectory
# This returns an [nframes x n_angles] array
xvar_val_array[file] = md.compute_angles(traj,ang_sub_arrays[str(i+1)])
# Convert to degrees:
xvar_val_array[file] *= (180/np.pi)
xlabel = f'{xvar_name} angle (degrees)'
elif n_particle_x == 4:
# Torsion
# Get torsion list
torsion_list = CGModel.get_torsion_list(cgmodel)
# Assign torsion types
torsion_types, torsion_array, torsion_sub_arrays, n_j, i_torsion_type, torsion_dict, inv_torsion_dict = \
assign_torsion_types(cgmodel, torsion_list)
# Set bin edges:
xvar_bin_edges = np.linspace(-180,180,nbin_xvar+1)
xvar_bin_centers = np.zeros((len(xvar_bin_edges)-1,1))
for i in range(len(xvar_bin_edges)-1):
xvar_bin_centers[i] = (xvar_bin_edges[i]+xvar_bin_edges[i+1])/2
for i in range(i_torsion_type):
if inv_torsion_dict[str(i+1)] == xvar_name or inv_torsion_dict[str(i+1)] == xvar_name_reverse:
# Compute all torsion values in trajectory
# This returns an [nframes x n_torsions] array
xvar_val_array[file] = md.compute_dihedrals(
traj,torsion_sub_arrays[str(i+1)])
# Convert to degrees:
xvar_val_array[file] *= (180/np.pi)
xlabel = f'{xvar_name} angle (degrees)'
# y variable
# Determine parameter type of yvar:
n_particle_y = yvar_name.count('_')+1
if n_particle_y == 2:
# Bond
# Get bond list
bond_list = CGModel.get_bond_list(cgmodel)
# Assign bond types:
bond_types, bond_array, bond_sub_arrays, n_i, i_bond_type, bond_dict, inv_bond_dict = \
assign_bond_types(cgmodel, bond_list)
for i in range(i_bond_type):
if inv_bond_dict[str(i+1)] == yvar_name or inv_bond_dict[str(i+1)] == yvar_name_reverse:
# Compute all bond length values in trajectory
# This returns an [nframes x n_bonds] array
yvar_val_array[file] = md.compute_distances(traj,bond_sub_arrays[str(i+1)])
# Get equilibrium value:
b_eq = cgmodel.get_bond_length(bond_sub_arrays[str(i+1)][0])
# Set bin edges:
# This should be the same across all files - use heuristic from equilibrium bond length
b_min = 0.5*b_eq.value_in_unit(unit.nanometer)
b_max = 1.5*b_eq.value_in_unit(unit.nanometer)
yvar_bin_edges = np.linspace(b_min,b_max,nbin_yvar+1)
yvar_bin_centers = np.zeros((len(yvar_bin_edges)-1,1))
for i in range(len(yvar_bin_edges)-1):
yvar_bin_centers[i] = (yvar_bin_edges[i]+yvar_bin_edges[i+1])/2
ylabel = f'{yvar_name} distance ({unit.nanometer})'
elif n_particle_y == 3:
# Angle
# Get angle list
angle_list = CGModel.get_bond_angle_list(cgmodel)
# Assign angle types:
ang_types, ang_array, ang_sub_arrays, n_i, i_angle_type, ang_dict, inv_ang_dict = \
assign_angle_types(cgmodel, angle_list)
# Set bin edges:
yvar_bin_edges = np.linspace(0,180,nbin_yvar+1)
yvar_bin_centers = np.zeros((len(yvar_bin_edges)-1,1))
for i in range(len(yvar_bin_edges)-1):
yvar_bin_centers[i] = (yvar_bin_edges[i]+yvar_bin_edges[i+1])/2
for i in range(i_angle_type):
if inv_ang_dict[str(i+1)] == yvar_name or inv_ang_dict[str(i+1)] == yvar_name_reverse:
# Compute all angle values in trajectory
# This returns an [nframes x n_angles] array
yvar_val_array[file] = md.compute_angles(traj,ang_sub_arrays[str(i+1)])
# Convert to degrees:
yvar_val_array[file] *= (180/np.pi)
ylabel = f'{yvar_name} angle (degrees)'
elif n_particle_y == 4:
# Torsion
# Get torsion list
torsion_list = CGModel.get_torsion_list(cgmodel)
# Assign torsion types
torsion_types, torsion_array, torsion_sub_arrays, n_j, i_torsion_type, torsion_dict, inv_torsion_dict = \
assign_torsion_types(cgmodel, torsion_list)
# Set bin edges:
yvar_bin_edges = np.linspace(-180,180,nbin_yvar+1)
yvar_bin_centers = np.zeros((len(yvar_bin_edges)-1,1))
for i in range(len(yvar_bin_edges)-1):
yvar_bin_centers[i] = (yvar_bin_edges[i]+yvar_bin_edges[i+1])/2
for i in range(i_torsion_type):
if inv_torsion_dict[str(i+1)] == yvar_name or inv_torsion_dict[str(i+1)] == yvar_name_reverse:
# Compute all torsion values in trajectory
# This returns an [nframes x n_torsions] array
yvar_val_array[file] = md.compute_dihedrals(
traj,torsion_sub_arrays[str(i+1)])
# Convert to degrees:
yvar_val_array[file] *= (180/np.pi)
ylabel = f'{yvar_name} angle (degrees)'
# Since the bonded variables may have different numbers of observables, we can use all
# combinations of the 2 parameter observables to create the histograms.
xvar_val_array_combo = {}
yvar_val_array_combo = {}
# Each array of single observables is [n_frames x n_occurances]
# x value arrays should be [xval0_y0, xval1_y0, ...xvaln_y0, ... xval0_yn, xval1_yn, xvaln_yn]
# y value arrays should be [yval0_x0, yval0_x1, ...yval0_xn, ... yvaln_x0, yvaln_x1, yvaln_xn]
for file in file_list:
n_occ_x = xvar_val_array[file].shape[1]
n_occ_y = yvar_val_array[file].shape[1]
xvar_val_array_combo[file] = np.zeros((nframes,n_occ_x*n_occ_y))
yvar_val_array_combo[file] = np.zeros_like(xvar_val_array_combo[file])
for iy in range(n_occ_y):
xvar_val_array_combo[file][:,(iy*n_occ_x):((iy+1)*n_occ_x)] = xvar_val_array[file]
for ix in range(n_occ_x):
yvar_val_array_combo[file][:,ix+iy*n_occ_x] = yvar_val_array[file][:,iy]
# Reshape arrays for histogramming:
xvar_val_array_combo[file] = np.reshape(xvar_val_array_combo[file], (nframes*n_occ_x*n_occ_y,1))
yvar_val_array_combo[file] = np.reshape(yvar_val_array_combo[file], (nframes*n_occ_x*n_occ_y,1))
# 2d histogram the data and plot:
hist_data, xedges, yedges = plot_2d_distribution(
file_list, xvar_val_array_combo, yvar_val_array_combo, xvar_bin_edges, yvar_bin_edges,
plotfile, colormap, xlabel, ylabel, temperature_list=temperature_list)
return hist_data, xedges, yedges
# Initiate cgmodel using positions from local file
positions = PDBFile("init.pdb").getPositions()
native_structure = positions
cgmodel = CGModel(
polymer_length=polymer_length,
backbone_lengths=backbone_lengths,
sidechain_lengths=sidechain_lengths,
sidechain_positions=sidechain_positions,
masses=masses,
sigmas=sigmas,
epsilons=epsilons,
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
torsion_force_constants=torsion_force_constants,
equil_torsion_angles=equil_torsion_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
positions=positions,
)
# Set parameters for definition/evaluation of native contacts
native_structure_contact_distance_cutoff = 1.05 * cgmodel.get_sigma(
0
) # This distance cutoff determines which nonbonded interactions are considered 'native' contacts
native_contact_cutoff_ratio = 1.1 # The distance ratio (in comparison with the distance of a contact in the native structure) below which a nonbonded interaction is considered 'native'
def assign_torsion_types(cgmodel, torsion_list):
"""Internal function for assigning torsion types"""
torsion_types = [] # List of torsion types for each torsion in torsion_list
torsion_array = np.zeros((len(torsion_list),4))
# Relevant torsion types are added to a dictionary as they are discovered
torsion_dict = {}
# Create an inverse dictionary for getting torsion string name from integer type
inv_torsion_dict = {}
# Counter for number of torsion types found:
i_torsion_type = 0
for i in range(len(torsion_list)):
torsion_array[i,0] = torsion_list[i][0]
torsion_array[i,1] = torsion_list[i][1]
torsion_array[i,2] = torsion_list[i][2]
torsion_array[i,3] = torsion_list[i][3]
particle_types = [
CGModel.get_particle_type_name(cgmodel,torsion_list[i][0]),
CGModel.get_particle_type_name(cgmodel,torsion_list[i][1]),
CGModel.get_particle_type_name(cgmodel,torsion_list[i][2]),
CGModel.get_particle_type_name(cgmodel,torsion_list[i][3])
]
string_name = ""
reverse_string_name = ""
for particle in particle_types:
string_name += f"{particle}_"
string_name = string_name[:-1]
for particle in reversed(particle_types):
reverse_string_name += f"{particle}_"
reverse_string_name = reverse_string_name[:-1]
if (string_name in torsion_dict.keys()) == False:
# New torsion type found, add to torsion dictionary
i_torsion_type += 1
torsion_dict[string_name] = i_torsion_type
torsion_dict[reverse_string_name] = i_torsion_type
# For inverse dict we will use only the forward name based on first encounter
inv_torsion_dict[str(i_torsion_type)] = string_name
# print(f"adding new torsion type {i_torsion_type}: {string_name} to dictionary")
# print(f"adding reverse version {i_torsion_type}: {reverse_string_name} to dictionary")
torsion_types.append(torsion_dict[string_name])
# Sort torsions by type into separate sub arrays for mdtraj compute_dihedrals
torsion_sub_arrays = {}
for i in range(i_torsion_type):
torsion_sub_arrays[str(i+1)] = np.zeros((torsion_types.count(i+1),4))
# Counter vector for all angle types
n_i = np.zeros((i_torsion_type,1), dtype=int)
for i in range(len(torsion_list)):
torsion_sub_arrays[str(torsion_types[i])][n_i[torsion_types[i]-1],:] = torsion_array[i,:]
n_i[torsion_types[i]-1] += 1
return torsion_types, torsion_array, torsion_sub_arrays, n_i, i_torsion_type, torsion_dict, inv_torsion_dict
def test_run_simulation(tmpdir):
"""Run a short MD simulation of a 24mer 1b1s model"""
# Set output directory
# In pytest we need to use a temp directory
# tmpdir is a fixture - hence we need to pass it into test function, not import it
output_directory = tmpdir.mkdir("output")
# OpenMM simulation settings
print_frequency = 10 # Number of steps to skip when printing output
total_simulation_time = 1.0 * unit.picosecond
simulation_time_step = 5.0 * unit.femtosecond
total_steps = int(np.floor(total_simulation_time / simulation_time_step))
temperature = 200 * unit.kelvin
friction = 1.0 / unit.picosecond
# Coarse grained model settings
include_bond_forces = True
include_bond_angle_forces = True
include_nonbonded_forces = True
include_torsion_forces = True
constrain_bonds = False
# Bond definitions
bond_length = 1.5 * unit.angstrom
bond_lengths = {
"bb_bb_bond_length": bond_length,
"bb_sc_bond_length": bond_length,
"sc_sc_bond_length": bond_length,
}
bond_force_constant = 1000 * unit.kilojoule_per_mole / unit.nanometer / unit.nanometer
bond_force_constants = {
"bb_bb_bond_force_constant": bond_force_constant,
"bb_sc_bond_force_constant": bond_force_constant,
"sc_sc_bond_force_constant": bond_force_constant,
}
# Particle definitions
mass = 100.0 * unit.amu
r_min = 1.5 * bond_length # Lennard-Jones potential r_min
# Factor of /(2.0**(1/6)) is applied to convert r_min to sigma
sigma = r_min / (2.0**(1.0 / 6.0))
epsilon = 0.5 * unit.kilojoule_per_mole
bb = {
"particle_type_name": "bb",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
sc = {
"particle_type_name": "sc",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
# Bond angle definitions
bond_angle_force_constant = 100 * unit.kilojoule_per_mole / unit.radian / unit.radian
bond_angle_force_constants = {
"bb_bb_bb_bond_angle_force_constant": bond_angle_force_constant,
"bb_bb_sc_bond_angle_force_constant": bond_angle_force_constant,
}
# OpenMM requires angle definitions in units of radians
bb_bb_bb_equil_bond_angle = 120.0 * unit.degrees
bb_bb_sc_equil_bond_angle = 120.0 * unit.degrees
equil_bond_angles = {
"bb_bb_bb_equil_bond_angle": bb_bb_bb_equil_bond_angle,
"bb_bb_sc_equil_bond_angle": bb_bb_sc_equil_bond_angle,
}
# Torsion angle definitions
torsion_force_constant = 20.0 * unit.kilojoule_per_mole
torsion_force_constants = {
"bb_bb_bb_bb_torsion_force_constant": torsion_force_constant,
"bb_bb_bb_sc_torsion_force_constant": torsion_force_constant
}
bb_bb_bb_bb_torsion_phase_angle = 75.0 * unit.degrees
bb_bb_bb_sc_torsion_phase_angle = 75.0 * unit.degrees
torsion_phase_angles = {
"bb_bb_bb_bb_torsion_phase_angle": bb_bb_bb_bb_torsion_phase_angle,
"bb_bb_bb_sc_torsion_phase_angle": bb_bb_bb_sc_torsion_phase_angle
}
torsion_periodicities = {
"bb_bb_bb_bb_torsion_periodicity": 3,
"bb_bb_bb_sc_torsion_periodicity": 3
}
# Monomer definitions
A = {
"monomer_name": "A",
"particle_sequence": [bb, sc],
"bond_list": [[0, 1]],
"start": 0,
"end": 0,
}
sequence = 24 * [A]
pdb_path = os.path.join(structures_path,
"24mer_1b1s_initial_structure.pdb")
positions = PDBFile(pdb_path).getPositions()
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
positions=positions,
sequence=sequence,
monomer_types=[A],
)
run_simulation(
cgmodel,
total_simulation_time,
simulation_time_step,
temperature,
friction=friction,
print_frequency=print_frequency,
output_directory=output_directory,
)
assert os.path.isfile(f"{output_directory}/simulation.dat")
assert os.path.isfile(f"{output_directory}/simulation.pdb")
os.mkdir(top_directory)
# OpenMM simulation settings
print_frequency = 20 # Number of steps to skip when printing output
total_simulation_time = 1.0 * unit.nanosecond # Units = picoseconds
simulation_time_step = 5.0 * unit.femtosecond
total_steps = round(total_simulation_time.__div__(simulation_time_step))
# Yank (replica exchange) simulation settings
number_replicas = 10
min_temp = 10.0 * unit.kelvin
max_temp = 50.0 * unit.kelvin
temperature_list = get_temperature_list(min_temp, max_temp, number_replicas)
print("Using " + str(len(temperature_list)) + " replicas.")
cgmodel = CGModel()
output_data = str(str(top_directory) + "/output.nc")
if not os.path.exists(output_data):
replica_energies, replica_positions, replica_states = run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
total_simulation_time=total_simulation_time,
print_frequency=print_frequency,
output_data=output_data,
)
else:
replica_energies, replica_positions, replica_states = read_replica_exchange_data(
def test_run_replica_exchange(tmpdir):
"""
Run a short replica exchange MD simulation of a 24mer 1b1s model
Test replica exchange processing (write pdb files)
Test heat capacity analysis code
Test physical validation code
"""
global_context_cache.platform = openmm.Platform.getPlatformByName("CPU")
# Set output directory
# In pytest we need to use a temp directory
# tmpdir is a fixture - hence we need to pass it into test function, not import it
output_directory = tmpdir.mkdir("output")
# Replica exchange simulation settings
total_simulation_time = 1.0 * unit.picosecond
simulation_time_step = 5.0 * unit.femtosecond
total_steps = int(np.floor(total_simulation_time / simulation_time_step))
output_data = os.path.join(output_directory, "output.nc")
number_replicas = 4
min_temp = 200.0 * unit.kelvin
max_temp = 300.0 * unit.kelvin
temperature_list = get_temperature_list(min_temp, max_temp,
number_replicas)
exchange_frequency = 10 # Number of steps between exchange attempts
# Coarse grained model settings
include_bond_forces = True
include_bond_angle_forces = True
include_nonbonded_forces = True
include_torsion_forces = True
constrain_bonds = False
# Bond definitions
bond_length = 1.5 * unit.angstrom
bond_lengths = {
"bb_bb_bond_length": bond_length,
"bb_sc_bond_length": bond_length,
"sc_sc_bond_length": bond_length,
}
bond_force_constant = 1000 * unit.kilojoule_per_mole / unit.nanometer / unit.nanometer
bond_force_constants = {
"bb_bb_bond_force_constant": bond_force_constant,
"bb_sc_bond_force_constant": bond_force_constant,
"sc_sc_bond_force_constant": bond_force_constant,
}
# Particle definitions
mass = 100.0 * unit.amu
r_min = 1.5 * bond_length # Lennard-Jones potential r_min
# Factor of /(2.0**(1/6)) is applied to convert r_min to sigma
sigma = r_min / (2.0**(1.0 / 6.0))
epsilon = 0.5 * unit.kilojoule_per_mole
bb = {
"particle_type_name": "bb",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
sc = {
"particle_type_name": "sc",
"sigma": sigma,
"epsilon": epsilon,
"mass": mass
}
# Bond angle definitions
bond_angle_force_constant = 100 * unit.kilojoule_per_mole / unit.radian / unit.radian
bond_angle_force_constants = {
"bb_bb_bb_bond_angle_force_constant": bond_angle_force_constant,
"bb_bb_sc_bond_angle_force_constant": bond_angle_force_constant,
}
# OpenMM requires angle definitions in units of radians
bb_bb_bb_equil_bond_angle = 120.0 * unit.degrees
bb_bb_sc_equil_bond_angle = 120.0 * unit.degrees
equil_bond_angles = {
"bb_bb_bb_equil_bond_angle": bb_bb_bb_equil_bond_angle,
"bb_bb_sc_equil_bond_angle": bb_bb_sc_equil_bond_angle,
}
# Torsion angle definitions
torsion_force_constant = 20.0 * unit.kilojoule_per_mole
torsion_force_constants = {
"bb_bb_bb_bb_torsion_force_constant": torsion_force_constant,
"bb_bb_bb_sc_torsion_force_constant": torsion_force_constant
}
bb_bb_bb_bb_torsion_phase_angle = 75.0 * unit.degrees
bb_bb_bb_sc_torsion_phase_angle = 75.0 * unit.degrees
torsion_phase_angles = {
"bb_bb_bb_bb_torsion_phase_angle": bb_bb_bb_bb_torsion_phase_angle,
"bb_bb_bb_sc_torsion_phase_angle": bb_bb_bb_sc_torsion_phase_angle
}
torsion_periodicities = {
"bb_bb_bb_bb_torsion_periodicity": 3,
"bb_bb_bb_sc_torsion_periodicity": 3
}
# Monomer definitions
A = {
"monomer_name": "A",
"particle_sequence": [bb, sc],
"bond_list": [[0, 1]],
"start": 0,
"end": 0,
}
sequence = 24 * [A]
pdb_path = os.path.join(structures_path,
"24mer_1b1s_initial_structure.pdb")
positions = PDBFile(pdb_path).getPositions()
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=[bb, sc],
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
torsion_force_constants=torsion_force_constants,
equil_bond_angles=equil_bond_angles,
torsion_phase_angles=torsion_phase_angles,
torsion_periodicities=torsion_periodicities,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
positions=positions,
sequence=sequence,
monomer_types=[A],
)
run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
total_simulation_time=total_simulation_time,
exchange_frequency=exchange_frequency,
output_data=output_data,
)
assert os.path.isfile(f"{output_directory}/output.nc")
# Process replica exchange output
# 1) With plot production only and print_timing:
replica_energies, replica_states, production_start, sample_spacing, n_transit, mixing_stats = process_replica_exchange_data(
output_data=output_data,
output_directory=output_directory,
plot_production_only=True,
print_timing=True,
)
# 2) With non-default equil_nskip
replica_energies, replica_states, production_start, sample_spacing, n_transit, mixing_stats = process_replica_exchange_data(
output_data=output_data,
output_directory=output_directory,
plot_production_only=True,
equil_nskip=2,
)
# 3) With frame_begin used to circumvent detectEquilibration
replica_energies, replica_states, production_start, sample_spacing, n_transit, mixing_stats = process_replica_exchange_data(
output_data=output_data,
output_directory=output_directory,
frame_begin=5,
)
# 4) With frame end specified to analyze only the beginning of a trajectory
replica_energies, replica_states, production_start, sample_spacing, n_transit, mixing_stats = process_replica_exchange_data(
output_data=output_data,
output_directory=output_directory,
frame_end=25,
)
# 5) Without writing .dat file:
replica_energies, replica_states, production_start, sample_spacing, n_transit, mixing_stats = process_replica_exchange_data(
output_data=output_data,
output_directory=output_directory,
write_data_file=False,
)
# Test pdb writer:
make_replica_pdb_files(
cgmodel.topology,
output_dir=output_directory,
)
make_state_pdb_files(cgmodel.topology, output_dir=output_directory)
assert os.path.isfile(f"{output_directory}/replica_4.pdb")
assert os.path.isfile(f"{output_directory}/state_4.pdb")
# With non-default frame_begin, stride, no centering:
make_replica_pdb_files(cgmodel.topology,
frame_begin=10,
frame_stride=2,
output_dir=output_directory)
make_state_pdb_files(cgmodel.topology,
frame_begin=10,
frame_stride=2,
output_dir=output_directory,
center=False)
# Test dcd writer:
make_replica_dcd_files(cgmodel.topology,
timestep=simulation_time_step,
time_interval=exchange_frequency,
output_dir=output_directory)
make_state_dcd_files(cgmodel.topology,
timestep=simulation_time_step,
time_interval=exchange_frequency,
output_dir=output_directory)
assert os.path.isfile(f"{output_directory}/replica_4.dcd")
assert os.path.isfile(f"{output_directory}/state_4.dcd")
# With non-default frame_begin, stride, no centering:
make_replica_dcd_files(cgmodel.topology,
timestep=simulation_time_step,
time_interval=exchange_frequency,
frame_begin=10,
frame_stride=2,
output_dir=output_directory)
make_state_dcd_files(cgmodel.topology,
timestep=simulation_time_step,
time_interval=exchange_frequency,
frame_begin=10,
frame_stride=2,
output_dir=output_directory,
center=False)
epsilon_list = [
unit.Quantity((0.25 + i * 0.25), unit.kilocalorie_per_mole)
for i in range(grid_size)
]
for epsilon in epsilon_list:
print(
"Performing simulations and heat capacity analysis for a coarse grained model"
)
print("with epsilon values of " + str(epsilon))
epsilons = {
"bb_bb_eps": epsilon,
"bb_sc_eps": epsilon,
"sc_sc_eps": epsilon
}
cgmodel = CGModel(epsilons=epsilons)
output_data = str(
str(top_directory) + "/eps_" + str(epsilon._value) + ".nc")
if not os.path.exists(output_data):
replica_energies, replica_positions, replica_states = run_replica_exchange(
cgmodel.topology,
cgmodel.system,
cgmodel.positions,
temperature_list=temperature_list,
simulation_time_step=simulation_time_step,
total_simulation_time=total_simulation_time,
print_frequency=print_frequency,
output_data=output_data,
)
else:
}
C = {
"monomer_name": "C",
"particle_sequence": [cbb, cbb, cbb, csc],
"bond_list": [[0, 1], [1, 2], [1, 3]], # sidechain is on 2nd bead
"start": 0,
"end": 2
}
sequence = 4 * [A, B, C]
# Build a coarse grained model
cgmodel = CGModel(
particle_type_list=particle_type_list,
bond_lengths=bond_lengths,
bond_force_constants=bond_force_constants,
bond_angle_force_constants=bond_angle_force_constants,
equil_bond_angles=equil_bond_angles,
include_nonbonded_forces=include_nonbonded_forces,
include_bond_forces=include_bond_forces,
include_bond_angle_forces=include_bond_angle_forces,
include_torsion_forces=include_torsion_forces,
constrain_bonds=constrain_bonds,
random_positions=random_positions,
sequence=sequence,
monomer_types=[A, B, C],
)
file_name = "12mer_ABC4s_initial_structure.pdb"
write_pdbfile_without_topology(cgmodel, file_name)
